DataPart1
Clementine
20/09/2018
#Code for CSD in census
ct <- "35535"
cm <- "24462"
cv <- "59933"
#Code for central municipality in census
cmt <- "3520005"
cmv <- "5915022"
cmm <- "2466023"
#Coordinates of city hall
cht <- c(-79.5257363, 43.6148863)
chv <- c(-123.2309412, 49.2219987)
chm <- c(-73.7171664, 45.4726094)
#Critical distance for AirBnb
bdtv <- 10000
bdm <- 12000
#Files from inside airbnb
f <- list.files('insideairbnb/')
## Identifying the vectors for visible Minority status
parent_vector <- "v_CA16_3954"
minorities <- list_census_vectors("CA16") %>%
filter(vector == "v_CA16_3954") %>%
child_census_vectors(leaves_only = TRUE) %>%
pull(vector)
minority_vectors <- c(parent_vector, minorities)
lab <- as.data.frame (list_census_vectors('CA16') %>%
filter(vector %in% minorities) %>%
select(vector, label))montrealAirbnb <- read.csv(paste0('insideairbnb/', f[1]))
torontoAirbnb <- read.csv(paste0('insideairbnb/', f[2]))
vancouverAirbnb <- read.csv(paste0('insideairbnb/', f[3]))
df <- rbind(montrealAirbnb, torontoAirbnb, vancouverAirbnb)l <- levels(df$property_type)
lookup <- data.frame('type' = 1:length(l))
lookup$type <- as.factor(l)
lookup$property_group <- c(
# [1] "Aparthotel" "Apartment" "Bed and breakfast" "Boat" "Boutique hotel" "Bungalow" "Cabin"
'hotel', 'home', 'hotel', 'other', 'hotel', 'home', 'other',
# [8] "Camper/RV" "Campsite" "Casa particular (Cuba)" "Cave" "Chalet" "Condominium" "Cottage"
'other', 'other', 'home', 'other', 'home', 'home', 'home',
# [15] "Farm stay" "Guest suite" "Guesthouse" "Hostel" "Hotel" "House" "Houseboat"
'home', 'home', 'hotel', 'hotel', 'hotel', 'home', 'home',
# [22] "Hut" "Loft" "Nature lodge" "Other" "Serviced apartment" "Tent" "Timeshare"
'other', 'home', 'other', 'other', 'hotel', 'other', 'other',
# [29] "Tiny house" "Townhouse" "Villa" "Barn" "Castle" "Dorm" "Earth house"
'home', 'home', 'home', 'home', 'home', 'hotel', 'other',
# [36] "In-law" "Parking Space" "Treehouse" "Resort"
'home', 'other', 'other', 'hotel'
)
dfhu <- left_join(df, lookup, by = c("property_type" = "type")) %>%
filter(property_group == 'home' &
as.character(host_neighbourhood) == as.character(neighbourhood) &
!duplicated(host_id))## Warning: Column `property_type`/`type` joining factors with different
## levels, coercing to character vector#df <- data.frame(df,lookup[match(df$property_type, lookup$type),] )
#dfh <- subset(df, property_group == 'home' & as.character(df$host_neighbourhood) == as.character(df$neighbourhood))
#dfhu <- dfh[!duplicated(dfh$host_id),]
#dim(dfh)[1] - dim(df)[1]
#dim(dfhu)[1] - dim(dfh)[1]
dim(dfhu)[1] - dim(df)[1]## [1] -19182dfhu$year <- as.numeric(substr(dfhu$last_review, 1, 4))
dfhun <- subset(dfhu, year >= 2017)
dim(dfhun)[1] - dim(dfhu)[1]## [1] -7415dfhun$numPrice <- as.numeric(gsub("[$]",'',dfhun$price))## Warning: NAs introduits lors de la conversion automatiquesummary(dfhun$room_type)## Entire home/apt Private room Shared room
## 11975 4497 142final <- subset(dfhun, room_type != "Shared room")
final$rooms <- ifelse(final$bedrooms == 0, 1, final$bedrooms)
final$priceperroom <- as.numeric(ifelse(final$room_type == T, final$numPrice, final$numPrice / final$rooms))
dim(final) / dim(df)## [1] 0.3811992 1.0520833Census data of all Canadian Metros
ggplot(bind_rows(cma_seg %>%
filter(Population > 100000) %>%
clean_names2 %>%
top_n(10, -H),
cma_seg %>%
filter(Population > 100000) %>%
clean_names2 %>%
top_n(10, H)),
aes(y = H, x = reorder(`Region Name`, H), size = Population)) +
#geom_bar(stat = "identity") +
geom_point(colour = "#7e008c") +
coord_flip() +
theme_ipsum() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(linetype = "dotted"),
legend.position = "none") +
labs(y = "Segregation entropy index", x = "",
#title = "The most and the least segregated large cities in Canada",
caption = "Segregation index of visible minorities\n@dshkol | Data: Statistics Canada, Census 2016")
ggplot(cma_seg%>% filter(Population > 100000) %>% clean_names2 %>%
mutate(big_cma = ifelse(`CMA Population` > 1000000, `CMA Name`,"Other")),
aes(y= H, x = Ei, size = Population^1.5, colour = big_cma)) +
#geom_label_repel(aes(label = `Region Name`)) +
geom_text_repel(aes(label = `Region Name`)) +
scale_size_continuous(guide = FALSE) +
scale_colour_ipsum("", guide = FALSE) +
theme_ipsum() +
theme(panel.grid = element_blank(),
axis.text = element_blank()) +
labs(x = "More diverse \u2192", y = "More segregated \u2192",
caption = "Entropy index based calculations of diversity and segregation\nof visible minority groups in cities with population over 100,000\n@dshkol | Data: Statistics Canada, Census 2016")
Start city specific analysis
city <- "toronto"
if(city == "toronto"){
citynames <- unique(torontoAirbnb$city)
censuscode <- ct
cityhall <- cht
censusCodeMuni <- cmt
breakDist <- bdtv
}
if(city == "vancouver"){
citynames <- unique(vancouverAirbnb$city)
censuscode <- cv
cityhall <- chv
censusCodeMuni <- cmv
breakDist <- bdtv
}
if(city == "montreal"){
citynames <- unique(montrealAirbnb$city)
censuscode <- cm
cityhall <- chm
censusCodeMuni <- cmm
breakDist <- bdm
}
city_data <- subset(final, city %in% citynames)tractTable <- diversity_index(censuscode)
pal <- colorQuantile(
palette = 'Blues',
domain = city_data$priceperroom,
n = 10
)
pal2 <- colorQuantile(
palette = 'Reds',
domain = tractTable$Ei,
n = 10)
map <- leaflet() %>% addProviderTiles("CartoDB.Positron") %>%
addPolygons(
data = csd.csd.geo,
color = 'black',
fill = F,
weight = 0.7,
opacity = 0.9
) %>% addPolygons(
data = tractTable,
color = ~ pal2(Ei),
fill = ~ pal2(Ei),
weight = 0.4
) %>%
#addCircleMarkers(
# data = city_data,
# radius = ~ sqrt(4 * rooms),
# lat = ~ latitude,
# fillColor = ~ pal(priceperroom),
# color = 'black',
# stroke = T,
# fillOpacity = 0.5,
# weight = 0.1,
# layerId = ~ id,
# lng = ~ longitude
# ) %>%
# addLegend(pal = pal, position = 'topleft', values = city_data$priceperroom)%>%
addLegend(pal = pal2, position = 'topleft', values = tractTable$Ei)
maplong.ct.tidy <- long.ct %>%
select(-White) %>%
tidyr::gather(Group, Count, `South Asian`:Multiple) %>%
mutate(Proportion = Count/Total)
ggplot(long.ct.tidy) + geom_sf(aes(fill = Proportion^(1/2), colour = Proportion^(1/2))) +
scale_fill_viridis_c(option = 3, guide = FALSE) +
scale_colour_viridis_c(option = 3, guide = FALSE) +
theme_void() +
coord_sf(datum = NA) +
facet_wrap(~Group, ncol = 4) +
labs(caption = "Visible minority groups by square-root proportion of Census Tract population\n@dshkol | Data: Statistics Canada, Census 2016")
pal <- colorQuantile(
palette = 'Blues',
domain = city_data$priceperroom,
n = 10
)
map <- leaflet() %>% addProviderTiles("CartoDB.Positron") %>%
addPolygons(
data = csd.csd.geo,
color = 'black',
fill = F,
weight = 0.7,
opacity = 0.9
) %>% addPolygons(
data = csd.geo,
color = 'grey',
fill = F,
weight = 0.4
) %>%
addCircleMarkers(
data = city_data,
radius = ~ sqrt(4 * rooms),
lat = ~ latitude,
fillColor = ~ pal(priceperroom),
color = 'black',
stroke = T,
fillOpacity = 0.5,
weight = 0.1,
layerId = ~ id,
lng = ~ longitude
) %>%
addLegend(pal = pal, position = 'topleft', values = city_data$priceperroom)## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette Blues is 9
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(max(3, n), palette): n too large, allowed maximum for palette Blues is 9
## Returning the palette you asked for with that many colorsmap#############aggregate airbnb into city tracts.
city_tr <- csd.geo[csd.geo$CSD_UID == censusCodeMuni,]
#city_tr$vacancy = (city_tr$Dwellings - city_tr$Households) / city_tr$Dwellings
#save(toronto_tr, vancouver_tr, file="cityTractsWithPopAndVacancy.RData")
rbnb_pts <- city_data
coordinates(rbnb_pts) <- ~longitude+latitude
city_tracts <- as_Spatial(city_tr)
rbnb_pts@proj4string <-CRS("+proj=longlat +datum=WGS84")
city_tracts@proj4string <-CRS("+proj=longlat +datum=WGS84")
plot(city_tracts, border = "grey")
points(rbnb_pts, col = "red", cex = 0.2, pch = 16)
#class(city_tracts)
#class(rbnb_pts)
PTS <- as(rbnb_pts, "sf")
POLY <- as(city_tracts, "sf")
idata <- st_intersection(PTS, POLY)## although coordinates are longitude/latitude, st_intersection assumes that they are planar## Warning: attribute variables are assumed to be spatially constant
## throughout all geometries#head(idata)
idata$distribPrice <- cut2(idata$priceperroom, g = 10)
intervals <- levels(unique(idata$distribPrice))
rbnbPerTract <- as.data.frame(idata %>%
#group_by(CSD_UID) %>%
count(GeoUID, sort = TRUE) %>%
select(GeoUID, n))[,1:2]
rbnbDistributionPerTract <- as.data.frame(idata %>%
#group_by(CSD_UID) %>%
count(GeoUID, distribPrice) %>%
select(GeoUID, distribPrice, n))[,1:3]
tractDistrib <- dcast(rbnbDistributionPerTract, GeoUID ~ distribPrice)## Using n as value column: use value.var to override.tractDistrib[is.na(tractDistrib)] <- 0
tractDistrib <- tractDistrib[,c("GeoUID", intervals)]
colnames(tractDistrib)[2:11] <- paste0("G", 1:10)
lookupDistrib <- data.frame('name' = paste0("G", 1:10), 'interval' = intervals)
listingCounts <- as.data.frame(rbnbPerTract[,1:2])
city_tracts@data <- data.frame(city_tracts@data,
listingCounts[match(city_tracts@data$GeoUID,
listingCounts$GeoUID),])
city_tracts@data <- data.frame(city_tracts@data,
tractDistrib[match(city_tracts@data$GeoUID,
tractDistrib$GeoUID),])
city_tracts@data <- data.frame(city_tracts@data,
tractTable[match(city_tracts@data$GeoUID,
tractTable$GeoUID),])Mino <- as.data.frame(cma.ct[,c("GeoUID", minorities)])
city_tracts@data <- data.frame(city_tracts@data,
Mino[match(city_tracts@data$GeoUID,
Mino$GeoUID),])
city_tracts@data$ListingPerCapita <- city_tracts@data$n / city_tracts@data$Population
city_tracts@data <- city_tracts@data %>%
mutate_at(minorities, funs(Share =./ Population) )
#summary(city_tracts@data)
centroidsCity <- gCentroid(city_tracts,byid=TRUE)
#plot(centroidsCity, add = T, pch = 16, cex = 0.6)
listMinoShares <- paste0(minorities, "_Share")
EstimatorAirbnbPresence <- cbind(city_tracts@data[,c("GeoUID", "ListingPerCapita", "Ei",
listMinoShares)],
coordinates(centroidsCity))
#head(EstimatorAirbnbPresence)
DistCityHall <- distm(EstimatorAirbnbPresence[,c('x','y')],
cityhall, fun=distVincentyEllipsoid)
EstimatorAirbnbPresence <- cbind(EstimatorAirbnbPresence, DistCityHall)
hist(EstimatorAirbnbPresence$DistCityHall)
#head(EstimatorAirbnbPresence)
ggplot(EstimatorAirbnbPresence, aes(x = DistCityHall, y = ListingPerCapita)) +
geom_point() + geom_smooth() + scale_x_log10() + scale_y_log10() +
geom_vline(xintercept = breakDist, col = "orange", cex = 1)
# mean value of listing per capita of tracts between 0 & 10 km:
central <- EstimatorAirbnbPresence %>%
filter(DistCityHall <= breakDist, !is.na(ListingPerCapita)) %>%
select(ListingPerCapita, DistCityHall, Ei, listMinoShares)
# regression listing per capita of tracts above 10 km:
peripheral <- EstimatorAirbnbPresence %>%
filter(DistCityHall > breakDist, !is.na(ListingPerCapita)) %>%
select(ListingPerCapita, DistCityHall, Ei, listMinoShares)for (sample in c("all", "central", "peripheral")){
if (sample == "all") df <- EstimatorAirbnbPresence
if (sample == "central") df <- central
if (sample == "peripheral") df <- peripheral
f <- paste0("scale(log(ListingPerCapita)) ~ scale(log(DistCityHall)) + ",
paste0("scale(",listMinoShares[-1], ")", collapse = " + "))
model <- lm(formula(f), data = df)
print(paste0("obs = ", dim(df)[1]))
print(summary(model))
coeffs <- as.data.frame(summary(model)$coefficients)
coeffs$label <- substr(rownames(coeffs), 7, 17)
res <- data.frame(coeffs, lab[match(coeffs$label, lab$vector),])
res[,c("label", "vector")] <- NULL
assign(paste0("Results_", sample), res)
}## [1] "obs = 572"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.54853 -0.51794 -0.01125 0.52926 2.30101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04765 0.03507 -1.359 0.174806
## scale(log(DistCityHall)) -0.07253 0.04704 -1.542 0.123714
## scale(v_CA16_3960_Share) -0.26653 0.05038 -5.291 1.83e-07 ***
## scale(v_CA16_3963_Share) -0.15991 0.04678 -3.419 0.000681 ***
## scale(v_CA16_3966_Share) -0.20791 0.05538 -3.754 0.000194 ***
## scale(v_CA16_3969_Share) -0.26156 0.04088 -6.398 3.63e-10 ***
## scale(v_CA16_3972_Share) -0.16263 0.05492 -2.962 0.003207 **
## scale(v_CA16_3975_Share) 0.01765 0.03999 0.442 0.659037
## scale(v_CA16_3978_Share) 0.10754 0.05931 1.813 0.070389 .
## scale(v_CA16_3981_Share) -0.16610 0.05065 -3.279 0.001113 **
## scale(v_CA16_3984_Share) 0.06116 0.04920 1.243 0.214358
## scale(v_CA16_3987_Share) 0.17337 0.03916 4.427 1.17e-05 ***
## scale(v_CA16_3990_Share) 0.04046 0.04838 0.836 0.403315
## scale(v_CA16_3993_Share) 0.10966 0.04433 2.474 0.013707 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7896 on 500 degrees of freedom
## (58 observations deleted due to missingness)
## Multiple R-squared: 0.3912, Adjusted R-squared: 0.3753
## F-statistic: 24.71 on 13 and 500 DF, p-value: < 2.2e-16
##
## [1] "obs = 129"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.47750 -0.51838 0.09242 0.44225 1.51489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01227 0.06281 -0.195 0.84543
## scale(log(DistCityHall)) 0.26285 0.07838 3.354 0.00108 **
## scale(v_CA16_3960_Share) -0.05530 0.07929 -0.698 0.48689
## scale(v_CA16_3963_Share) 0.44399 0.07910 5.613 1.42e-07 ***
## scale(v_CA16_3966_Share) -0.29703 0.09290 -3.197 0.00180 **
## scale(v_CA16_3969_Share) -0.04331 0.08340 -0.519 0.60457
## scale(v_CA16_3972_Share) -0.12145 0.08968 -1.354 0.17832
## scale(v_CA16_3975_Share) 0.19020 0.07179 2.650 0.00920 **
## scale(v_CA16_3978_Share) -0.04714 0.09046 -0.521 0.60326
## scale(v_CA16_3981_Share) -0.16192 0.07353 -2.202 0.02967 *
## scale(v_CA16_3984_Share) -0.13654 0.07289 -1.873 0.06361 .
## scale(v_CA16_3987_Share) 0.09813 0.06953 1.411 0.16088
## scale(v_CA16_3990_Share) 0.22111 0.07619 2.902 0.00445 **
## scale(v_CA16_3993_Share) 0.04013 0.07854 0.511 0.61042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7105 on 114 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.5459, Adjusted R-squared: 0.4941
## F-statistic: 10.54 on 13 and 114 DF, p-value: 2.39e-14
##
## [1] "obs = 386"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.55191 -0.43016 -0.00474 0.44182 2.07480
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.623e-16 3.495e-02 0.000 1.000000
## scale(log(DistCityHall)) -5.083e-01 5.122e-02 -9.925 < 2e-16 ***
## scale(v_CA16_3960_Share) -1.422e-01 4.962e-02 -2.866 0.004398 **
## scale(v_CA16_3963_Share) -4.279e-02 4.479e-02 -0.955 0.340043
## scale(v_CA16_3966_Share) 2.990e-03 5.644e-02 0.053 0.957782
## scale(v_CA16_3969_Share) -2.048e-01 4.051e-02 -5.055 6.76e-07 ***
## scale(v_CA16_3972_Share) -2.893e-01 5.449e-02 -5.309 1.90e-07 ***
## scale(v_CA16_3975_Share) -6.089e-03 3.764e-02 -0.162 0.871593
## scale(v_CA16_3978_Share) -1.614e-02 5.056e-02 -0.319 0.749748
## scale(v_CA16_3981_Share) -1.773e-01 5.248e-02 -3.379 0.000805 ***
## scale(v_CA16_3984_Share) 1.069e-01 5.329e-02 2.006 0.045603 *
## scale(v_CA16_3987_Share) 1.470e-01 4.048e-02 3.631 0.000322 ***
## scale(v_CA16_3990_Share) -1.055e-02 5.432e-02 -0.194 0.846094
## scale(v_CA16_3993_Share) 1.332e-01 4.259e-02 3.127 0.001905 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6867 on 372 degrees of freedom
## Multiple R-squared: 0.5444, Adjusted R-squared: 0.5284
## F-statistic: 34.19 on 13 and 372 DF, p-value: < 2.2e-16#str(Results_all)
reg_total <- cbind(Results_all,Results_central, Results_peripheral)
reg_total[,c("t.value", "Std..Error", "t.value.1", "Std..Error.1", "label.1.1", "t.value.2", "Std..Error.2", "label.1.2")] <- NULL
colnames(reg_total) <- c("est_all", "pval_all", "minority", "est_central", "pval_central",
"est_peripheral", "pval_peripheral")
reg_total$variables <- rownames(reg_total)
reg_total$var <- ifelse(is.na(reg_total$minority), as.character(reg_total$variables), as.character(reg_total$minority))pall <- ggplot(reg_total, aes(x = var)) +
geom_bar(aes(y = est_all, fill = ifelse(pval_all < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
# scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank())
pcent <- ggplot(reg_total, aes(x = var)) +
geom_bar(aes(y = est_central, fill = ifelse(pval_central < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
# scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank() )
pper <- ggplot(reg_total, aes(x = var)) +
geom_bar(aes(y = est_peripheral, fill = ifelse(pval_peripheral < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
#scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank())
grid.arrange(pall, pcent, pper)
EstimatorAirbnbPrice <- data.frame(idata, EstimatorAirbnbPresence[match(idata$GeoUID,
EstimatorAirbnbPresence$GeoUID),])
# head(EstimatorAirbnbPrice)
# hist(EstimatorAirbnbPrice$priceperroom)
EstimatorAirbnbPrice_all <- EstimatorAirbnbPrice %>%
filter(!is.na(priceperroom), priceperroom > 0)
# mean value of listing per capita of tracts between 0 & 10 km:
central_P <- EstimatorAirbnbPrice_all %>%
filter(DistCityHall <= breakDist) #%>% select(ListingPerCapita, DistCityHall, Ei, listMinoShares)
peripheral_P <- EstimatorAirbnbPrice_all %>%
filter(DistCityHall > breakDist) #%>% select(ListingPerCapita, DistCityHall, Ei, listMinoShares)
for (sample in c("all", "central", "peripheral")){
if (sample == "all") df <- EstimatorAirbnbPrice_all
if (sample == "central") df <- central_P
if (sample == "peripheral") df <- peripheral_P
f <- paste0("scale(priceperroom) ~ scale(log(DistCityHall)) + ",
paste0("scale(",listMinoShares[-1], ")", collapse = " + "))
model <- lm(formula(f), data = df)
print(paste0("obs = ", dim(df)[1]))
print(summary(model))
coeffs <- as.data.frame(summary(model)$coefficients)
coeffs$label <- substr(rownames(coeffs), 7, 17)
res <- data.frame(coeffs, lab[match(coeffs$label, lab$vector),])
res[,c("label", "vector")] <- NULL
assign(paste0("Results_", sample, "Price"), res)
}## [1] "obs = 6657"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1962 -0.6065 -0.1791 0.3766 10.5641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0001742 0.0117322 0.015 0.98815
## scale(log(DistCityHall)) -0.0668709 0.0149279 -4.480 7.60e-06 ***
## scale(v_CA16_3960_Share) -0.0032345 0.0146358 -0.221 0.82510
## scale(v_CA16_3963_Share) -0.0295906 0.0150768 -1.963 0.04973 *
## scale(v_CA16_3966_Share) -0.0963053 0.0157190 -6.127 9.49e-10 ***
## scale(v_CA16_3969_Share) -0.1373439 0.0137331 -10.001 < 2e-16 ***
## scale(v_CA16_3972_Share) -0.0646569 0.0154912 -4.174 3.03e-05 ***
## scale(v_CA16_3975_Share) 0.2015608 0.0141764 14.218 < 2e-16 ***
## scale(v_CA16_3978_Share) 0.0195717 0.0152841 1.281 0.20040
## scale(v_CA16_3981_Share) -0.0272682 0.0185182 -1.473 0.14093
## scale(v_CA16_3984_Share) -0.0086998 0.0182519 -0.477 0.63363
## scale(v_CA16_3987_Share) -0.0098371 0.0130038 -0.756 0.44939
## scale(v_CA16_3990_Share) -0.0386677 0.0141334 -2.736 0.00624 **
## scale(v_CA16_3993_Share) 0.0860938 0.0135781 6.341 2.44e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9572 on 6642 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.08565, Adjusted R-squared: 0.08386
## F-statistic: 47.86 on 13 and 6642 DF, p-value: < 2.2e-16
##
## [1] "obs = 1865"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1669 -0.6003 -0.1782 0.3840 8.9505
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0002938 0.0220161 0.013 0.9894
## scale(log(DistCityHall)) 0.1539406 0.0270725 5.686 1.51e-08 ***
## scale(v_CA16_3960_Share) -0.0202564 0.0327393 -0.619 0.5362
## scale(v_CA16_3963_Share) 0.1265478 0.0294063 4.303 1.77e-05 ***
## scale(v_CA16_3966_Share) -0.0611201 0.0294178 -2.078 0.0379 *
## scale(v_CA16_3969_Share) 0.1293594 0.0318532 4.061 5.09e-05 ***
## scale(v_CA16_3972_Share) -0.1237398 0.0289276 -4.278 1.99e-05 ***
## scale(v_CA16_3975_Share) 0.2395442 0.0310037 7.726 1.80e-14 ***
## scale(v_CA16_3978_Share) -0.0508944 0.0334406 -1.522 0.1282
## scale(v_CA16_3981_Share) -0.0371391 0.0307432 -1.208 0.2272
## scale(v_CA16_3984_Share) -0.0178721 0.0267618 -0.668 0.5043
## scale(v_CA16_3987_Share) 0.0212608 0.0238212 0.893 0.3722
## scale(v_CA16_3990_Share) 0.0150129 0.0305890 0.491 0.6236
## scale(v_CA16_3993_Share) 0.0290583 0.0291923 0.995 0.3197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9505 on 1850 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1029, Adjusted R-squared: 0.09657
## F-statistic: 16.32 on 13 and 1850 DF, p-value: < 2.2e-16
##
## [1] "obs = 4792"
##
## Call:
## lm(formula = formula(f), data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0444 -0.5511 -0.1503 0.3427 9.6695
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.087e-15 1.316e-02 0.000 1.00000
## scale(log(DistCityHall)) -3.644e-01 1.796e-02 -20.282 < 2e-16 ***
## scale(v_CA16_3960_Share) 6.234e-02 1.784e-02 3.494 0.00048 ***
## scale(v_CA16_3963_Share) -2.973e-02 1.638e-02 -1.815 0.06963 .
## scale(v_CA16_3966_Share) -5.403e-02 1.975e-02 -2.735 0.00626 **
## scale(v_CA16_3969_Share) -8.783e-02 1.610e-02 -5.457 5.10e-08 ***
## scale(v_CA16_3972_Share) -1.186e-01 1.831e-02 -6.480 1.01e-10 ***
## scale(v_CA16_3975_Share) 1.143e-01 1.583e-02 7.219 6.08e-13 ***
## scale(v_CA16_3978_Share) -8.425e-03 1.696e-02 -0.497 0.61943
## scale(v_CA16_3981_Share) 2.647e-02 2.146e-02 1.234 0.21741
## scale(v_CA16_3984_Share) 3.419e-03 2.125e-02 0.161 0.87216
## scale(v_CA16_3987_Share) -4.345e-02 1.475e-02 -2.945 0.00324 **
## scale(v_CA16_3990_Share) -6.594e-03 2.017e-02 -0.327 0.74379
## scale(v_CA16_3993_Share) 6.013e-02 1.479e-02 4.067 4.84e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9111 on 4778 degrees of freedom
## Multiple R-squared: 0.1721, Adjusted R-squared: 0.1698
## F-statistic: 76.39 on 13 and 4778 DF, p-value: < 2.2e-16reg_total2 <- cbind(Results_allPrice,Results_centralPrice, Results_peripheralPrice)
reg_total2[,c("t.value", "Std..Error", "t.value.1", "Std..Error.1", "label.1.1", "t.value.2", "Std..Error.2", "label.1.2")] <- NULL
colnames(reg_total2) <- c("est_all", "pval_all", "minority", "est_central", "pval_central",
"est_peripheral", "pval_peripheral")
reg_total2$variables <- rownames(reg_total2)
reg_total2$var <- ifelse(is.na(reg_total2$minority), as.character(reg_total2$variables),
as.character(reg_total2$minority))pall2 <- ggplot(reg_total2, aes(x = var)) +
geom_bar(aes(y = est_all, fill = ifelse(pval_all < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
# scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank())
pcent2 <- ggplot(reg_total2, aes(x = var)) +
geom_bar(aes(y = est_central, fill = ifelse(pval_central < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
# scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() + theme(legend.title=element_blank() )
pper2 <- ggplot(reg_total2, aes(x = var)) +
geom_bar(aes(y = est_peripheral, fill = ifelse(pval_peripheral < 0.05, "pval < 0.05", "pval >= 0.05")), stat="identity") +
#scale_y_continuous(limits = c(-0.55, 0.55)) +
coord_flip() +
theme(legend.title=element_blank())
grid.arrange(pall, pall2,
pcent, pcent2,
pper, pper2,
ncol = 2)
ReardonSegregationAirbnbPrice <- segIndex10(tabOfSpatialUnits = city_tracts@data, distributionColNames = paste0("G", 1:10))
EntropySegregationMinorities <- cma_seg[cma_seg$CSD_UID == censusCodeMuni,"H"]
ReardonSegregationAirbnbPrice## [1] 0.2631066EntropySegregationMinorities## # A tibble: 1 x 1
## H
## <dbl>
## 1 0.180